In the past, machinery and devices would have to be specifically be programmed to carry out their designated task. If the outcome was not desirable, adjustments would need to be manually made and the machine re-programmed. Through Machine Learning, systems are developed with the ability to learn and improve through experience without the need for specific programming.
Through access to data, a computer programme can now learn and continue to improve outcomes without the need for human interventions.
What does this mean for healthcare?
Usually assigned as an application of artificial intelligence (AI), machine learning is still a comparatively recent discovery but already making a strong and significant impact in healthcare. In particular, when referring to personalised medicine which is completely reliant on the ability to analyse large amounts of data. Through machine learning, we now have the ability to efficiently analyse thousands of documents and patient records; speeding up time in the lab and access of care to patients.
Machine Learning and the overall use of AI has in many instances divided the healthcare industry in particular on issues concerning cyber security and access of personal data. As efficient clinical trials rely on access to large amounts of, often sensitive, patient data; the use of automated systems also increases the risk of cyber-attacks or technical failure which comprises the safeguarding of sensitive information.